The present application relates generally to computers and computer applications, and more particularly to anomaly or change detection in manufacturing process or environment equipped with sensors.
Manufacturing systems and equipments deteriorate over time. Predicting equipment or system failure, which for instance may occur as a result of wear and tear degradation, is not a trivial problem. While manufacturing facilities produce sensor data to aid in determining problems and diagnoses, data from such facilities are multidimensional or multi-way. Multi-way array is also referred to as a tensor. Change detection or diagnoses using such multidimensional data is challenging.
A virtual metrology predicts a performance metric as a function of sensor recordings monitoring a manufacturing process. Such virtual metrology methods use a technique called unfolding (or vectorization), which converts an input tensor to a vector. However, using a vector for diagnoses is not easy, for example, because the conversion destroys the original structure of data and produces a very high dimensional vector.